Unmanned aerial vehicles (UAVs) are remotely piloted or self-piloted aircraft that can carry sensors, communications equipment, or other payloads. They have been used in a reconnaissance and intelligence-gathering role for many years. More recently, UAVs have been developed for the purpose of surveillance and target tracking. Autonomous surveillance and target tracking performed by UAVs is either military or civilian environments is becoming an important aspect of intelligence-gathering methods.
Typically, UAVs use the Global Positioning System (GPS) to provide navigation over various terrains. In many scenarios, UAVs are required to fly in an urban environment, which often have an urban canyon effect. An “urban canyon” is an artifact of an urban environment similar to a natural canyon. The urban canyon is caused by streets cutting through dense blocks of high rise buildings such as skyscrapers. It is known that the urban canyon can cause problems in reception of GPS signals.
For example, GPS receivers can suffer from multipath errors which are caused from receiving a composite of direct GPS signals and reflected GPS signals from nearby objects such as buildings. In an urban canyon, a direct line of sight (LOS) GPS signal can be completely blocked by a building structure and a reflected signal can reach the receiver, resulting in position and velocity errors of significant magnitudes. Thus, when UAVs operate in an urban environment, access to the GPS signals needed to navigate without drift is often denied by the urban canyon.
Many methods have been proposed for using image-aided navigation of autonomous vehicles such as UAVs. For example, matching or referencing a GPS-denied vehicle's current image of terrain to a satellite image of the same terrain has been suggested. However, these methods lack the temporal and spatial proximity needed in order to use the images for UAV navigation in an urban canyon.